1. Field of the Invention
The present invention relates to optimizing the placement of wells in an oilfield. More specifically, the present invention relates to well placement optimization and to location and placement of multilateral wells in producing hydrocarbon reservoirs.
2. Description of the Related Art
Nonconventional or multilateral wells (hereinafter called “multilateral”) have become more utilized in the last two decades in producing hydrocarbon reservoirs. The main advantage of using such wells against conventional and single bore wells comes from the additional access to reservoir rock by maximizing the reservoir contact. Multilateral wells have evolved in the petroleum industry with more complex designs and architecture, (such as what are known as Extended Reach wells, Maximum Reservoir Contact wells, and Extreme Reservoir Contact wells). The challenge associated with deploying multilateral wells has been whether the trajectories for the main bore, laterals, and sublaterals are being placed in the best spots in the reservoir. In other words, the design for placement of multilateral wells should be optimum to efficiently drain hydrocarbon reservoirs.
Finding the best multilateral well design and location has been the topic of several studies. Some of the approaches have been used were Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). For example, as described in “Optimization of Nonconventional Well Type, Location and Trajectory”, SPE 77865, September 2002, a GA approach was used as the main optimization engine to find the optimum multilateral location and design. To deal with the situation of long and large number of runs, helper algorithms were used to accelerate the optimization procedure. On a well level scale, the parameters to optimize were the coordinates that define the heel and the toe of the laterals in a complex well, the length of the lateral in horizontal plane, the angle between the lateral and the x-axis, the number of junctions the mother bore may have, and the number of laterals that may originate from any junction. The objective function was cumulative oil or Net Present Value (NPV) and the resultant types of multilateral well depend mainly on type of the reservoir and degree of uncertainty.
As described in “Optimization of Nonconventional Wells Under Uncertainty Using Statistical Proxies,” Computational Geosciences, Vol. 10, No. 4, pp. 389-404, 2006, V. Artus, L. J. Durlofsky, J. Onwunalu, and K. Aziz and in “Optimization of Nonconventional Well Placement Using Genetic Algorithms and Statistical Proxy,” J. Onwunalu, 2006, the GA approach was combined with a statistical proxy that was described as a clustering based technique. This technique was used to establish relationships between the variables and the objective function (i.e. NPV) using unsupervised learning methods.
The combined approach was applied to find optimum location of a dual lateral well using the same parameterization described in “Optimization of Nonconventional Well Type, Location and Trajectory, SPE 77565, Annual Technical Conference and Exhibition, September 2002, Y. Burak, L. Durlofsky, and A. Khalid, which was also used in “Optimization of Multilateral Well Design and Location in a Real Field Using a Continuous Genetic Algorithm” SPE 136944, Annual Technical Conference and Exhibition, 2010, with continuous GA instead of binary GA.
“A New Well-Pattern-Optimization Procedure for Large-Scale Field Development,” SPE 124364, September, 2011, J. E. Onwunalu and L. J. Durlofsky, described well pattern optimization that included a well pattern description, followed by well perturbation, that are incorporated into particle swarm organization or PSO as the core optimizer. Two dual laterals location optimization were investigated. Due to the large number of variables that were needed to be parameterized for each well, the computational expense was high, which resulted in wells with single mother bores.
“Optimal Multilateral Well Placement,” Oxford University Mathematical Institute, No. 10, pp. 1-13, 2010, C. L. Farmer, J. M. Fowkes, and N. I. M. Gould described a proxy from reservoir simulator output by using a radial basis function model, coupled with a Branch and Bound (B&B) global optimizer to find optimum location and trajectory for single multilateral well. The determined resultant location was compared with GA results and showed advantage in using B&B by achieving higher objective function values. The parameterization of the multilateral well was the same described in SPE 77565.
The methods discussed above were able to find trajectories and locations for small number of laterals and limited number of multilateral wells. However, the laterals formed were built as straight lines which might not reflect a practical well associated with actual reservoir complexity. So far as is known, none of the foregoing work dealt successfully with locating the best hydrocarbon rich areas and the optimum trajectories for large groups of multilateral wells.
Other methods of well placement have also been disclosed. For example, U.S. Published Patent Application No. 2011/0015909 was concerned with modeling subterranean reservoir models with many underground details obtained from processing geological and geophysical data. Reservoir details were assigned as needed, such as heterogeneity data, natural fractures, faults, tight reservoir streaks, vugs, shale bodies, discontinuous shale bodies and boundary conditions. Wellbore trajectories which were predetermined from well drilling reports were also integrated in the model.
U.S. Published Patent Application No. 2013/0024174 described a procedure to rank selected range criteria, such as be bounds on oil or gas volume, permeability, saturation, relative permeability, minimum oil saturation, maximum gas oil ratio or maximum water cut and the like. Based on the selected criteria, a certain number of cells were highlighted. From the highlighted cells, connected ones were reported. Drainage volume was found for the groups of connected cells, and a property cut-off was applied to eliminate cells with very low permeability values. Oil or gas in place was calculated for every group of connected cells which can be sorted afterwards from high to low oil or gas in place values. After multiple adjustments to drainable volumes cells (i.e. distance from a boundary and tortuosity of a connected volume) and sorting, the final completion intervals were revealed which were the candidate targets to be penetrated by wells.
Wells were generated for the candidate targets with a mathematical formulation, with the constraints used being selected from well characterization criteria from wells in place. Optimum wells found by the formulation were validated with streamline simulation and the output values were used as new set of constraints for the next new wells.